The use of vibration analysis as a Predictive Maintenance (PDM) tool for monitoring the health of rotating machinery is a well established practice. In general, this process utilizes intelligent data collection instruments that are carried by a technician from machine to machine in accordance with a preprogrammed "route" to acquire the vibration signatures for each machine. A typical route will include a list of machines, test points for each machine, and a specification of how data is to be acquired for each test point. Generally, the vibration data collected consists of the spectral data, obtained using a fast Fourier Transform (FFT). The spectrum is generally broken up into analysis parameters containing the energy in selected frequency or order-based bands and these parameters are checked against alarm limits established for each point. These analysis parameters of the entire spectral data set can be stored in the data collector and later downloaded to a database on a workstation for storage and further analysis.
The spectral data stored typically is average data which is acquired from multiple blocks of data where each block of data is transformed to the frequency domain via FFT and averaged in the frequency domain. The averaged spectral data does not require excessive storage capability relative to the amount of time data required to construct the FFT and hence, can be stored in the portable data collectors which can store average spectral data from a few hundred measurement points. As an example, consider a single measurement point consisting of a 400-line spectra computed from 6 averages. For each spectral block, a time data block consisting of 1024 time data points must be collected. Since the stored average spectral data block is made up of 6 block averages, it requires 6.times.1024, or 6144 time data points. This is far too much data to store and manage given the number of measurement points that are typically monitored at a given facility on a measurement route.
Although the acquisition of averaged vibration spectral data using portable data collectors in the "route" mode has proven extremely valuable for machinery monitoring in predictive maintenance programs, it is generally acknowledged that time data would provide highly useful data to assist in the interpretation of certain classes of problems commonly experienced in machinery. However, saving all of the time data used to construct the spectral data is simply too burdensome to be considered a realistic option.
What is needed, therefore, is an efficient and convenient methodology for computing a few key parameters which characterize the time data in a manner which enhances the spectral data that is routinely acquired for machine health or condition monitoring. These time waveform parameters can be checked against alarm limits to alert the technician that unusual conditions in the time waveform have been detected. This automatic prompting of the technician can enable further investigation of the possible causes and can automatically trigger storage of the time waveform data for further analysis by an analyst at the analysis workstation.
In general, the evaluation of time waveform data from vibration sensors has been done by an experienced human eye, if done at all. The systematic analysis of vibration time waveforms by the computer has not been done except to look at overall amplitude type parameters. The approach described herein is to look for waveform behaviors that are indicative of various classes of faults by calculating a set of waveform parameters that are metrics of the possible behaviors in the waveform data. These waveform parameters are quantitative and can be used to automate the analysis of waveform data and to determine the direction and rate at which changes are occurring.